With the spread of the monitoring video and the increasing focus on the safety issues, there is an urgent need for an intelligent analysis for a specific object, such as a pedestrian or a vehicle, in the monitoring video data.
Taking the pedestrian detection as an example, in a pedestrian detection method, a video scenario is segmented into blobs and each blob is assumed to include only one pedestrian, and the pedestrian is tracked in unit of blob. The method is effective for a scenario in which the density of the pedestrians is relatively low. However, for a scenario in which there is complicated traffic or the density of the pedestrians is high, each blob may include many pedestrians, therefore, it is difficult for such an algorithm to locate each pedestrian accurately.
In another pedestrian detection method, a pedestrian detection algorithm is performed directly for each frame of the video. However, such a method is complicated and requires a long computation time. Further, for a monitoring scenario in which there is large traffic, since there is a lot of shielding situations in the scenario, it is also difficult to locate each pedestrian in the scenario completely.